# Prediction Model of Hydropower Generation and Its Economic Benefits Based on EEMD-ADAM-GRU Fusion Model

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## Abstract

**:**

## 1. Introduction

## 2. Methods

#### 2.1. EEMD Model

#### 2.2. GRU Model

#### 2.3. ADAM Optimization Algorithm

#### 2.4. Construction of Fusion Prediction Model

## 3. Case Study

## 4. Conclusions

- (1)
- As an improved RNN neural network, the GRU model can conduct deep mining on the change rule of time series and derive high-precision prediction accuracy on the time series of hydropower generation. EEMD signal decomposition and ADAM model parameter optimization can further improve the prediction accuracy of the GRU model. Compared with the conventional time series prediction model, the average prediction error is reduced by more than 10%.
- (2)
- The economic benefits of hydropower generation measure only the direct economic benefits. In addition, the hydropower price is replaced by the national average hydropower unit price, which does not reflect the fluctuation of hydropower prices with months. It should study the pricing mechanism and fluctuation law of hydropower prices in all provinces of the country in the future, so as to accurately measure the economic benefits of hydropower generation.
- (3)
- The proposed prediction model is based on the concept of the “signal decomposition +parameter optimization+ prediction model”. There are many types of signal decomposition methods and prediction models in existing research results. This paper only selects representative methods to combine and analyze. In future research, it is necessary to expand the model selection and analyze the prediction accuracy of different model combinations to propose a more universal prediction method.

## Author Contributions

## Funding

## Institutional Review Board Statement

## Informed Consent Statement

## Data Availability Statement

## Conflicts of Interest

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Parameter | Value | Parameter | Value |
---|---|---|---|

Max epochs | 250 | Learn rate schedule | ‘piecewise’ |

Gradient threshold | 1 | Learn rate drop period | 125 |

Initial learn rate | 0.0005 | Learn rate drop factor | 0.2 |

Number of hidden layers | 1 | Number of neurons | 128 |

Active function | Sigmoid | Dropout | 0.2 |

Comparison Model | Parameter Title | Value |
---|---|---|

NARNET | Number of neurons | 30 |

Feedback mode | ‘Open’ | |

Train function | ‘trainlm’ | |

LSTM | Max epochs | 250 |

Gradient threshold | 1 | |

Initial learn rate | 0.0005 | |

Learn rate schedule | ‘piecewise’ | |

Learn rate drop period | 125 | |

Learn rate drop factor | 0.2 | |

AR | Compound AR polynomial degree | 15 |

Compound MA polynomial degree | 0 | |

Degree of nonseasonal integration | 0 | |

Degree of seasonal differencing polynomial | 0 | |

ARIMA | Compound AR polynomial degree | 4 |

Compound MA polynomial degree | 8 | |

Degree of nonseasonal integration | 1 | |

Degree of seasonal differencing polynomial | 0 | |

VAR | Multivariate autoregressive polynomial order | 10 |

Model | RSME/Billion kWh | Decline Ratio | SD/billion kWh | Decline Ratio |
---|---|---|---|---|

NARNET | 179.34 | 16.16% | 324.32 | 15.69% |

EEMD-LSTM | 189.24 | 20.55% | 338.32 | 19.18% |

EEMD-ADAM-GRU | 150.35 | / | 273.42 | / |

AR | 171.04 | 12.10% | 310.43 | 11.92% |

ARIMA | 183.28 | 17.97% | 323.56 | 15.50% |

VAR | 163.33 | 7.95% | 298.21 | 8.31% |

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**MDPI and ACS Style**

Wang, J.; Gao, Z.; Ma, Y. Prediction Model of Hydropower Generation and Its Economic Benefits Based on EEMD-ADAM-GRU Fusion Model. *Water* **2022**, *14*, 3896.
https://doi.org/10.3390/w14233896

**AMA Style**

Wang J, Gao Z, Ma Y. Prediction Model of Hydropower Generation and Its Economic Benefits Based on EEMD-ADAM-GRU Fusion Model. *Water*. 2022; 14(23):3896.
https://doi.org/10.3390/w14233896

**Chicago/Turabian Style**

Wang, Jiechen, Zhimei Gao, and Yan Ma. 2022. "Prediction Model of Hydropower Generation and Its Economic Benefits Based on EEMD-ADAM-GRU Fusion Model" *Water* 14, no. 23: 3896.
https://doi.org/10.3390/w14233896